diff --git a/.gitignore b/.gitignore index f23bbc7..65fc7c8 100644 --- a/.gitignore +++ b/.gitignore @@ -20,3 +20,4 @@ utilities/__pycache__/ fftma_module/gen/log_* fftma_module/gen/out*.npy .ipynb_checkpoints/analysis-checkpoint.ipynb +fftma_module/gen/.ipynb_checkpoints/ diff --git a/fftma_module/gen/analysis.ipynb b/fftma_module/gen/analysis.ipynb index 77aac67..3298186 100644 --- a/fftma_module/gen/analysis.ipynb +++ b/fftma_module/gen/analysis.ipynb @@ -9,7 +9,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -34,7 +34,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -44,7 +44,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -89,7 +89,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -101,7 +101,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -1305,44 +1305,327 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 94, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "0\n", - "1\n", - "2\n", - "3\n", - "4\n", - "5\n", - "6\n", - "7\n", - "8\n", - "9\n" + "Executing file number 1 out of 10\n", + "Executing file number 2 out of 10\n", + "Executing file number 3 out of 10\n", + "Executing file number 4 out of 10\n", + "Executing file number 5 out of 10\n", + "Executing file number 6 out of 10\n", + "Executing file number 7 out of 10\n", + "Executing file number 8 out of 10\n", + "Executing file number 9 out of 10\n", + "Executing file number 10 out of 10\n" ] } ], "source": [ "dfs = []\n", "for i in range(10):\n", - " print(i)\n", + " print(\"Executing file number {} out of 10\".format(i+1))\n", " df = create_df(\"log_256_{}.txt\".format(i+1))\n", " dfs.append(df)" ] }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 95, "metadata": {}, "outputs": [], "source": [ - "functions = ['Py_kgeneration', 'generate', 'fftma2', 'covariance', 'gasdev', 'fourt', 'cov_value', 'ran2', 'build_real', 'prebuild_gwn', 'clean_real', 'cgrid', 'length', 'maxfactor']\n", + "def merge_dfs(dfs):\n", + " functions = ['Py_kgeneration', 'generate', 'fftma2', 'covariance', 'gasdev', 'fourt', 'cov_value', 'ran2', 'build_real', 'prebuild_gwn', 'clean_real', 'cgrid', 'length', 'maxfactor']\n", + " df_final = pd.concat(dfs, join='inner').sort_values(by=('time', 'sum'), ascending=False) \n", "\n", + " memory_min, memory_max, memory_median = [], [], []\n", + " time_min, time_max, time_mean, time_sum, time_count = [], [], [], [], []\n", "\n", - "#df_final = pd.concat(dfs).sort_values(by=('time', 'sum'), ascending=False) " + " for function in functions:\n", + " memory_min.append(df_final.loc[function, ('memory', 'min')].min())\n", + " time_min.append(df_final.loc[function, ('time', 'min')].min())\n", + " memory_max.append(df_final.loc[function, ('memory', 'max')].max())\n", + " time_max.append(df_final.loc[function, ('time', 'max')].max())\n", + " time_mean.append(df_final.loc[function, ('time', 'mean')].mean())\n", + " time_sum.append(df_final.loc[function, ('time', 'sum')].sum())\n", + " time_count.append(df_final.loc[function, ('time', 'count')].sum())\n", + " try:\n", + " memory_median.append(df_final.loc[function, ('memory', 'median')].median())\n", + " except:\n", + " memory_median.append(df_final.loc[function, ('memory', 'median')])\n", + " \n", + " df = pd.DataFrame({('memory', 'min'): memory_min, ('memory', 'max'): memory_max, ('memory', 'median'): memory_median, ('time', 'min'): time_min, ('time', 'max'): time_max, ('time', 'mean'): time_mean, ('time', 'sum'): time_sum, ('time', 'count'): time_count})\n", + "\n", + " df.index = functions\n", + " df.index.name = 'function'\n", + " return df" + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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gasdev8.70.0-13.516777216.00.0013580.0000330.000000564.182445
fourt11.5-1.4-16.23.08.4298296.3784545.01500619.135362
cov_value0.70.0-13.98855600.00.0004370.0000020.00000121.579349
ran20.90.0-0.821359556.00.0003810.0000020.00000045.002553
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prebuild_gwn6.56.56.51.00.1081600.1081600.1081600.108160
clean_real127.2127.2127.21.00.0952670.0952670.0952670.095267
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length0.00.00.03.00.0000430.0000340.0000210.000102
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" + ], + "text/plain": [ + " memory time \\\n", + " max median min count max mean \n", + "function \n", + "Py_kgeneration 7421.6 7421.6 7421.6 1.0 1226.822575 1226.822575 \n", + "generate 6691.7 6691.7 6691.7 1.0 959.799368 959.799368 \n", + "fftma2 872.0 872.0 872.0 1.0 267.021516 267.021516 \n", + "covariance 870.5 870.5 870.5 1.0 247.512194 247.512194 \n", + "gasdev 8.7 0.0 -13.5 16777216.0 0.001358 0.000033 \n", + "fourt 11.5 -1.4 -16.2 3.0 8.429829 6.378454 \n", + "cov_value 0.7 0.0 -13.9 8855600.0 0.000437 0.000002 \n", + "ran2 0.9 0.0 -0.8 21359556.0 0.000381 0.000002 \n", + "build_real -0.2 -0.2 -0.2 1.0 0.151968 0.151968 \n", + "prebuild_gwn 6.5 6.5 6.5 1.0 0.108160 0.108160 \n", + "clean_real 127.2 127.2 127.2 1.0 0.095267 0.095267 \n", + "cgrid 0.0 0.0 0.0 1.0 0.000160 0.000160 \n", + "length 0.0 0.0 0.0 3.0 0.000043 0.000034 \n", + "maxfactor 0.0 0.0 0.0 5.0 0.000002 0.000002 \n", + "\n", + " \n", + " min sum \n", + "function \n", + "Py_kgeneration 1226.822575 1226.822575 \n", + "generate 959.799368 959.799368 \n", + "fftma2 267.021516 267.021516 \n", + "covariance 247.512194 247.512194 \n", + "gasdev 0.000000 564.182445 \n", + "fourt 5.015006 19.135362 \n", + "cov_value 0.000001 21.579349 \n", + "ran2 0.000000 45.002553 \n", + "build_real 0.151968 0.151968 \n", + "prebuild_gwn 0.108160 0.108160 \n", + "clean_real 0.095267 0.095267 \n", + "cgrid 0.000160 0.000160 \n", + "length 0.000021 0.000102 \n", + "maxfactor 0.000001 0.000008 " + ] + }, + "execution_count": 96, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "merge_dfs(dfs)" ] }, {